1. Field of the Invention
The present invention relates to a face detecting apparatus and a face detecting program, for discriminating whether human faces are included in images.
2. Description of the Related Art
The basic principle of face detection, for example, is classification into two classes, either a class of faces or a class not of faces. A technique called “boosting” is commonly used as a classification method for classifying faces. The boosting algorithm is a learning method for classifiers that links a plurality of weak classifiers to form a single strong classifier. Edge data within the planes of multiple resolution images are employed as characteristic amounts used for classification by the weak classifiers.
U.S. Patent Application Publication No. 20020102024, S. Lao et al., “Fast Omni-Directional Face Detection”, MIRU2004, pp. II271-II276, July 2004, and S. Li and Z. Zhang, “Float Boost Learning and Statistical Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 9, pp. 1-12, September 2004 disclose methods that speeds up face detecting processes by the boosting technique. In these methods, the weak classifiers are provided in a cascade structure, and only images which have been judged to represent faces by upstream weak classifiers are subject to judgment by downstream weak classifiers.
A detection rate, a false positive detection rate, and processing speed are three criteria when evaluating the detection performance of the aforementioned classifiers. In order to improve the detection performance of classifiers, it is desired for the detection rate to be increased, the false positive detection rate to be decreased, and the processing speed to be increased. Here, the “detection rate” refers to the percentage of correct discriminations regarding whether an image represents a face. The “false positive detection rate” refers to a percentage of erroneous detections, when images that do not represent faces are detected as faces. The “processing speed” refers to the speed at which discrimination is performed after an image is input. These three evaluative criteria are correlated. If the detection rate is increased, the false positive detection rate increases, and the processing speed decreases. If the false positive detection rate is decreased, the detection rate decreases, and the processing speed increases. If the processing speed is increased, the detection rate decreases, and the false positive detection rate decreases.
Meanwhile, face detecting processes are performed for a variety of purposes, such as skin color correction and red eye correction. Different detection performances are desired for each purpose. Accordingly, there is a problem that a face detecting apparatus having a detection performance suited for general use cannot perform optimal face detection suited for each purpose.